% THIS IS SIGPROC-SP.TEX - VERSION 3.1
% WORKS WITH V3.2SP OF ACM_PROC_ARTICLE-SP.CLS
% APRIL 2009
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% It is an example file showing how to use the 'acm_proc_article-sp.cls' V3.2SP
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% This .tex file (and associated .cls V3.2SP) *DOES NOT* produce:
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% you need to 'insert'  your .bbl file into your source .tex file so as to provide
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% Questions regarding SIGS should be sent to
% Adrienne Griscti ---> griscti@acm.org
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% Questions/suggestions regarding the guidelines, .tex and .cls files, etc. to
% Gerald Murray ---> murray@hq.acm.org
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% For tracking purposes - this is V3.1SP - APRIL 2009
\documentclass{vldb}
\usepackage{amsmath}
\usepackage{color}
\usepackage{graphicx}
\usepackage{balance} 
\usepackage{setspace} 
\usepackage{spverbatim} 

\usepackage{algorithm}
\usepackage{algorithmic} 
%\newtheorem{example}{Example}
\newtheorem{definition}{Definition}
\newtheorem{theorem}{Theorem}
\newtheorem{myproof}{Proof}
%\newtheorem{assumption}{Assumption}
\newtheorem{lemma}{Lemma}

\newcommand{\todo}[1]{\textbf{\textcolor{red}{@TODO: #1}}}
 
\newcommand{\argmax}{\operatornamewithlimits{argmax}}

\begin{document}
  
\title{SERIMI: Class-based Matching for Instance Matching Across Heterogeneous Datasets}
\toappear{}

%\begin{document}

%\title{SERIMI: Class-based Disambiguation for Effective Instance Matching over Heterogeneous Web Data}
 
%
% You need the command \numberofauthors to handle the 'placement
% and alignment' of the authors beneath the title.
%
% For aesthetic reasons, we recommend 'three authors at a time'
% i.e. three 'name/affiliation blocks' be placed beneath the title.
%
% NOTE: You are NOT restricted in how many 'rows' of
% "name/affiliations" may appear. We just ask that you restrict
% the number of 'columns' to three.
%
% Because of the available 'opening page real-estate'
% we ask you to refrain from putting more than six authors
% (two rows with three columns) beneath the article title.
% More than six makes the first-page appear very cluttered indeed.
%
% Use the \alignauthor commands to handle the names
% and affiliations for an 'aesthetic maximum' of six authors.
% Add names, affiliations, addresses for
% the seventh etc. author(s) as the argument for the
% \additionalauthors command.
% These 'additional authors' will be output/set for you
% without further effort on your part as the last section in
% the body of your article BEFORE References or any Appendices.

\numberofauthors{3} %  in this sample file, there are a *total*
% of EIGHT authors. SIX appear on the 'first-page' (for formatting
% reasons) and the remaining two appear in the \additionalauthors section.
%
\author{
% You can go ahead and credit any number of authors here,
% e.g. one 'row of three' or two rows (consisting of one row of three
% and a second row of one, two or three).
%
% The command \alignauthor (no curly braces needed) should
% precede each author name, affiliation/snail-mail address and
% e-mail address. Additionally, tag each line of
% affiliation/address with \affaddr, and tag the
% e-mail address with \email.
% 
% 1st. author
\alignauthor 
Samur Araujo \\
     \affaddr{Delft University of Technology}\\     
       \affaddr{Delft, the Netherlands}\\
       \email{s.f.cardosodearaujo@tudelft.nl}
% 2nd. author
\alignauthor Duc Thanh Tran \\
       \affaddr{Karlsruher Institute of Technology}\\ 
       \affaddr{ Germany}\\
       \email{ducthanh.tran@kit.edu}       
% 3rd. author
\alignauthor 
Arjen P. de Vries \\
     \affaddr{Delft University of Technology}\\     
       \affaddr{Delft, the Netherlands}\\
       \email{a.p.devries@itudelft.nl}\\
       \and 
% 4th. author
\alignauthor 
Daniel Schwabe \\
       \affaddr{Informatics Department PUC-Rio}\\     
       \affaddr{ Rio de Janeiro, Brazil}\\
       \email{dschwabe@inf.puc-rio.br}
}
\maketitle
\begin{abstract}  
%We study the problem of detecting different instance representations that refer to the same real world entity, also called \emph{instance matching}. 
Based on a detailed analysis, we observed that state-of-the-art instance matching approaches do not perform well when used for matching instances \emph{across heterogeneous datasets}. This is because they are built upon \emph{direct matching}, which involves a direct comparison of 
%instances in the 
a source dataset with 
%instances in the 
a target dataset. This 
%matching paradigm 
is not suitable when the overlap between the datasets is too small. 
%, which is often the case with heterogeneous data. 
%to provide sufficient cues for a direct comparison. 
Aiming at this problem, we propose a new paradigm called \textit{class-based matching}. 
%, which we use in combination with direct matching. 
Given a class of instances from the source dataset, called the \emph{class of interest}, and a set of candidate matches retrieved from the target, 
%(via direct matching), 
class-based matching helps to refine the candidates by filtering out those that do not belong to the class of interest. For this refinement, only data in the target is used, i.e., no direct comparison between source and target is involved. 
%Besides the main idea, we also discuss optimizations to \emph{compactly represent the class of interest} for greater efficiency and a method to \emph{automatically select the threshold} for filtering matches more effectively. 
Based on extensive experiments using 
%ly evaluate our approach
%, called SERIMI, 
%using two 
public benchmarks, 
we show our approach greatly improves the results of state-of-the-art systems especially on hard matching tasks.  
% and several other state-of-the-art systems not covered by the benchmarks. The results suggest that SERIMI uses valuable 
%These \emph{extensive experiments} show that SERIMI yields superior results. The class-based matching achieved competitive results when compared to the direct matching; and most importantly, it was complementary to it when the direct matching presented a low performance. In average, SERIMI outperformed all baselines.  \todo{i added more about results. not number because they are not so impressive.}
\end{abstract}  

%\category{H.2.4}{Database Management}{Systems}
%\category{H.2.5}{Database Management}{Heterogeneous Databases}
% 
%\keywords{data integration, instance matching, linked data} % NOT required for Proceedings
% 
\input{sec-introduction}

\input{sec-overview}
%\input{sec-relations}
\input{sec-approach}
\input{sec-reduction}
\input{sec-threshold}
\input{sec-evaluation1} 
\input{sec-evaluation2}
\input{sec-tables}
\input{sec-related}

\section{Conclusion}
 
In this work, we propose an unsupervised instance matching approach that combines direct-based matching with a novel class-based matching technique to infer Sameas relation over heterogeneous data. This method focuses on determining similarity between instances, specially when there is not enough overlapping among source and target instances. Also, we propose an efficient class-based matching algorithm and a method that uses a statistic outlier detection strategy to eliminate false positive matches from a set of candidates matches. 
We evaluated our method using two public benchmarks: OAEI 2010 and 2011. The results show that we achieved good and competitive results compared to several representative systems focused on instance matching over heterogeneous data.
 

\bibliographystyle{abbrv}
\bibliography{journal}

\input{sec-appendix}


\end{document}
